AI Agent Framework Comparison (LangGraph/CrewAI/AutoGen)

What is an agent framework?

An agent framework is a software library or runtime environment that goes beyond mere LLM API calls. In the Blck Alpaca reading, it bundles at least four of the following six building blocks: an agent abstraction (instruction, tools and model as a composable unit), a tool/function-calling layer including error handling, state management across multiple steps, orchestration for multi-step or multi-agent flows, observability/tracing for debugging and auditing, as well as production hooks such as human-in-the-loop, guardrails and streaming.

The vendor positionings differ in their emphasis: in "Building Effective Agents" (December 2024), Anthropic distinguishes workflows (predefined code paths) from agents that dynamically steer their own steps. LangChain describes LangGraph as a "low-level orchestration framework […] for long-running, stateful agents". Microsoft speaks of a "unified agent framework" with an enterprise foundation. This page provides a vendor-neutral overview of the most important frameworks for decision-makers and tech leads in the DACH region.

The three classics: LangGraph, CrewAI, AutoGen

LangGraph (LangChain Inc., US) models agents as a graph of nodes and edges and is designed for stateful, long-running production. The current version is 1.2.0 (12 May 2026). Strengths are the low-level control, durable execution, human-in-the-loop, mature observability via LangSmith and the broadest toolchain via the LangChain ecosystem (750+ tools). Weaknesses are the learning curve due to the graph mental model, a certain verbosity (according to third-party analyses, 120 instead of 40 lines of code for a ReAct agent) and the LangChain dependency tree. According to the LangChain repository, Klarna, Replit, Uber, LinkedIn and Elastic, among others, use LangGraph in production.

CrewAI (crewAI Inc., US; v1.10.1, Q1 2026) pursues a role-based mental model of crews and event-driven flows. It is Python-only, designed as a standalone (no LangChain legacy) and supports MCP as well as A2A natively. Its strength is the fastest multi-agent prototype; weaknesses are an abstraction that becomes opaque with more than five agents and a token overhead of around 18 percent in 3-agent configurations, according to third-party analyses. In the 2026 State of Agentic AI Survey (n=500 C-level, revenue over 100 million USD, seven regions, Business Wire, 11 Feb 2026), CrewAI claims that 60% of the US Fortune 500 use the framework — a vendor claim that has not been independently audited.

AutoGen lives on in its successor: according to the Microsoft roadmap, AutoGen is in maintenance mode, with the strategic focus on the Microsoft Agent Framework, in which AutoGen concepts are integrated into a new graph workflow engine (techcommunity.microsoft.com, April 2026). Anyone starting fresh on AutoGen today should plan ahead for the migration path to the Microsoft Agent Framework.

The provider SDKs: OpenAI, Anthropic, Microsoft

OpenAI Agents SDK (launch 11 March 2025) is a lightweight, Python-first framework with minimal abstractions. The update "The next evolution of the Agents SDK" (15 April 2026) added native sandbox execution and a model-native harness for long-running tasks. Strengths are the cheapest time-to-first-demo, the visual AgentKit builder as well as first-class support for MCP and AGENTS.md. Weaknesses are the vendor pull toward OpenAI models, the still v0.x API and the deprecation of the Assistants API in mid-2026, which forces a migration.

Claude Agent SDK emerged from the Claude Code SDK on 29 September 2025 and offers a high-quality agent harness with subagents, sessions, hooks as well as integrated permissions and sandbox. MCP is natively supported. Important for the license assessment: the code is under Apache 2.0, but usage is under the Anthropic Commercial Terms — not a pure OSS mindset. The SDK is bound to Claude models and is still in the v0.x phase with breaking changes.

Microsoft Agent Framework has been General Available since 3 April 2026 (Public Preview from 1 October 2025), is under the MIT license and comes with an explicit LTS commitment. It converges Semantic Kernel and AutoGen (whose predecessor projects together had over 75,000 GitHub stars), offers first-class .NET support alongside Python, native A2A and MCP support as well as six providers with a one-line swap (Azure OpenAI, OpenAI, Anthropic, Bedrock, Gemini, Ollama). Weaknesses are the youth of the GA release and the suspicion of lock-in through the tight integration with Azure AI Foundry.

It is also worth looking at two further frameworks: Pydantic AI (v1.95.0, 13 May 2026, MIT) focuses on type safety, provider agnosticism and native observability via Logfire in an EU region. n8n (n8n GmbH, Berlin) is the only DACH-developed solution with native agent nodes on a LangChain basis and a fair-code license.

Comparison table

As of: 13 May 2026. Version states and star counts are snapshots in time.

Selection criteria: which framework when?

The choice follows a decision path. First the Anthropic question: Do I even need a framework? With only one or two tools without state, the direct LLM API call is often sufficient. From around three tools plus state, a framework is worthwhile.

• .NET/C# mandatory? → Microsoft Agent Framework.
• Already LangChain/LangSmith in the stack, or audit obligation and long-running workflows? → LangGraph.
• GPT-only and the fastest demo path? → OpenAI Agents SDK.
• Anthropic models as a strategic choice, coding/long-running agents? → Claude Agent SDK.
• Fastest multi-agent prototyping with a role/crew model? → CrewAI.
• Type safety and engineering discipline over time-to-demo? → Pydantic AI.
• Low-code, citizen developer or strict DACH sovereignty? → n8n.

A common anti-pattern is the framework choice before the use-case definition. Equally risky are vendor lock-in via proprietary state management, ignoring the protocol layers MCP and A2A as well as underestimating observability. Keep prompts, tools and eval suites as framework-agnostic as possible to make switching easier.

Protocols decouple the choice: MCP and A2A

Since the donation of the Model Context Protocol (MCP) to the Linux Foundation Agentic AI Foundation (9 December 2025) and the A2A v1.0 GA (9 April 2026 with over 150 sponsoring organizations, over 22,000 GitHub stars and five SDK languages), the framework choice can be decoupled. MCP standardizes tool access (over 110 million monthly SDK downloads and over 10,000 active servers, as of April 2026); A2A standardizes agent-to-agent communication.

Native MCP support is offered by CrewAI, OpenAI Agents SDK, Microsoft Agent Framework, Claude Agent SDK and Pydantic AI; LangGraph and n8n use adapters. Native A2A is brought by Microsoft Agent Framework, CrewAI and Pydantic AI. Anyone who wants to avoid lock-in prioritizes protocol-compliant frameworks.

DACH relevance: sovereignty, hosting and the EU AI Act

Note: the following statements are informational and not legal advice. GDPR and EU AI Act conformity is case-dependent and must be coordinated with data protection/compliance as well as with qualified legal professionals.

Fully self-hostable open-source frameworks (LangGraph, CrewAI Core, Pydantic AI, n8n) can be operated on European hosters such as STACKIT (Schwarz), IONOS Cloud or OVHcloud and thus offer high sovereignty. n8n is additionally vendor-sovereign as a Berlin-based vendor. SDKs proprietarily tied to US LLM providers (OpenAI Agents SDK, Claude Agent SDK) only achieve strict EU data residency via paths such as Azure OpenAI (EU) or AWS Bedrock (EU). The Microsoft Agent Framework runs in Azure EU regions (Frankfurt, West Europe, Switzerland North, Sweden Central) under the Microsoft EU Data Boundary; the CLOUD Act discussion, however, remains.

On the EU AI Act: the framework has been in force since August 2024; the high-risk obligations were originally planned to apply from August 2026. The "Digital Omnibus" compromise of 7 May 2026 provides for a postponement to 2 December 2027, but has not yet been formally adopted (provisional, to be re-verified before any decision). For high-risk deployments with requirements for logging, traceability and human oversight, LangGraph plus LangSmith, Microsoft Agent Framework plus Application Insights as well as Pydantic AI plus Logfire offer OTel-compliant tracing paths.

Outlook and practical note

The market is consolidating along open protocols rather than closed ecosystems: MCP and A2A are becoming the common language, AutoGen is being absorbed into the Microsoft Agent Framework, and the provider SDKs are maturing toward sandbox and durable execution. For practice, a mixed model rather than a monoculture is advisable — for example n8n for workflow automation, LangGraph or CrewAI self-hosted for code-first agents and Pydantic AI with Logfire EU region for type-safe Python production. Start with the use case and a lean proof-of-concept, keep prompts and tools framework-agnostic, and re-check version states, star counts and the EU AI Act status shortly before each commitment.